The COVID-19 pandemic has implications beyond physical health, affecting society and economies. Government efforts to slow down the spread of the virus have had a severe impact on many businesses, including restaurants. Mandatory policies such as restaurant closures, bans on social gatherings, and social distancing restrictions have affected restaurant operations as well as customer preferences (e.g., prompting a demand of stricter hygiene standards). As of now, however, it is not clear how and to what extent the pandemic has affected restaurant reviews, an analysis of which could potentially inform policies for addressing this ongoing situation. In this work, we present our efforts to understand the effects of COVID-19 on restaurant reviews, with a focus on Yelp reviews produced during the pandemic for New York City and Los Angeles County restaurants. Overall, we make the following contributions. First, we assemble a dataset of 600 reviews with manual annotations of fine-grained COVID-19 aspects related to restaurants (e.g., hygiene practices, service changes, sympathy and support for local businesses). Second, we address COVID-19 aspect detection using supervised classifiers, weakly-supervised approaches based on keywords, and unsupervised topic modeling approaches, and experimentally show that classifiers based on pre-trained BERT representations achieve the best performance (F1=0.79). Third, we analyze the number and evolution of COVID-related aspects over time and show that the resulting time series have substantial correlation (Spearman’s 𝜌=0.84) with critical statistics related to the COVID-19 pandemic, including the number of new COVID-19 cases. To our knowledge, this is the first work analyzing the effects of COVID-19 on Yelp restaurant reviews and could potentially inform policies by public health departments, for example, to cover resource utilization.